Finding the longest common subsequence for multiple biological sequences by ant colony optimization

Shyu, Shyong Jian; Tsai, Chun-Yuan

doi:10.1016/j.cor.2007.07.006

Cited by 49 publications

(25 citation statements)

References 28 publications

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“…With regard to the set of instances provided by Shyu and Tsai [22], we repeat the same analysis. That is, we fine-tune the parameters of Bs and graphically show the results in Fig.…”

Section: Comparison With Acomentioning

confidence: 99%

“…It is composed of 50 instances per combination of |Σ|, n, and l. These instances were created by sequentially generating each letter with a probability of 1/|Σ|. A second set of instances comes from Shyu and Tsai [22]. Their instances are biologically inspired, and thus, they considered alphabet sizes |Σ| = 4, corresponding to DNA sequences, and |Σ| = 20, corresponding to protein sequences.…”

Section: Benchmark Instancesmentioning

confidence: 99%

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Beam search for the longest common subsequence problem

Blum

Blesa

López-Ibáñez

2009

Computers & Operations Research

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The longest common subsequence problem is a classical string problem that concerns finding the common part of a set of strings. It has several important applications, for example, in pattern recognition or computational biology. Most research efforts up to now have focused on solving this problem optimally. In comparison, only few works exist dealing with heuristic approaches. In this work we present a deterministic beam search algorithm. The results show that our algorithm outperforms classical approaches as well as recent metaheuristic approaches.

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“…With regard to the set of instances provided by Shyu and Tsai [22], we repeat the same analysis. That is, we fine-tune the parameters of Bs and graphically show the results in Fig.…”

Section: Comparison With Acomentioning

confidence: 99%

Section: Benchmark Instancesmentioning

confidence: 99%

Beam search for the longest common subsequence problem

Blum

Blesa

López-Ibáñez

2009

Computers & Operations Research

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“…Apart from the results of Beam-ACO we also present the results of the standard ACO algorithm proposed by Shyu and Tsai in [17] and the results of beam search [3], which is the current stateof-the-art algorithm. For ACO we provide for each instance the information that was given in [17], that is, the average results and the average computation times over 10 runs, as well as the corresponding standard deviations. For beam search, which is a deterministic heuristic, we provide the result in addition to the computation time.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…The experimental results that we outline in the following were obtained on a PC with an AMD64X2 4400 processor and 4 Gigabyte of memory. The set of benchmark instances that we chose for testing was introduced by Shyu and Tsai in [17]. Their instances are biologically inspired, and thus, they consider alphabets of size |Σ| = 4, corresponding to DNA sequences, and of size |Σ| = 20, corresponding to protein sequences.…”

Section: Experimental Evaluationmentioning

confidence: 99%

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Beam-ACO for the longest common subsequence problem

Blum

2010

IEEE Congress on Evolutionary Computation

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Abstract-The longest common subsequence problem is classical string problem. It has applications, for example, in pattern recognition and bioinformatics. In this work we present a socalled Beam-ACO approach for solving this problem. Beam-ACO algorithms are hybrid techniques that results from a combination of ant colony optimization and beam search, which is an incomplete branch and bound derivative. Our results show that Beam-ACO is able to find new best solutions for 31 out of 60 benchmark instances that we chose for the experimental evaluation of the algorithm. I. INTRODUCTIONThe longest common subsequence (LCS) problem is one of the classical string problems. Given a problem instance (S, Σ), where S = {s 1 , s 2 , . . . , s n } is a set of n strings over a finite alphabet Σ, the problem consists in finding a longest string t * that is a subsequence of all the strings in S. Such a string t * is called a longest common subsequence of the strings in S. Note that a string t is called a subsequence of a string s, if t can be produced from s by deleting characters. For example, dga is a subsequence of adagtta. Due to its classical nature, the LCS problem has attracted quite a lot of research efforts over the past decades. Much work has been dedicated to the development of efficient dynamic programming procedures (see, for example, [2]). The body of work on approximate methods is dominated by constructive one-pass heuristics [9], [10]. Moreover, metaheuristics have been proposed in [17], [8], [12]. In [3] we recently published the current state-of-the-art algorithm for the LCS problem. The results of this algorithm, which is based on beam search (BS), have shown that none of the earlier algorithms came even close to derive good solutions for difficult problem instances. In other words, BS has shown to be largely superior to any other existing technique for what concerns the LCS problem. In this work we try to improve over the state-of-the-art results of BS by adding a learning component to BS. The resulting algorithm is a hybrid between the metaheuristic ant colony optimization (ACO) [7] and beam search. The interested reader might note that we already published a preliminary version of this

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2016

Metaheuristics for String Problems in Bio‐Informatics

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Finding the longest common subsequence for multiple biological sequences by ant colony optimization

Cited by 49 publications

References 28 publications

Beam search for the longest common subsequence problem

Beam search for the longest common subsequence problem

Beam-ACO for the longest common subsequence problem

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